This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. A central focus of cancer genetics is the study of mutations that are causally implicated in tumorigenesis. These mutations not only provide insights into cancer biology but also present anti-cancer therapeutic targets and diagnostic markers. Several major cancer genome projects are currently underway to profile genetic changes in large collections of tumor samples. However, a majority of the changes identified in such screens are non-functional passenger mutations. Even with current technologies, it remains a challenge to distinguish causal cancer mutations from other harmless genetic alterations or passenger mutations. We have developed a novel method that uses known cancer-associated variants to build a general description of a cancer mutation. This method is described in detail in our Cancer Research paper. Three different algorithms are used to generate measurements describing cancer-associated variants. These measurements are then used to construct a random forest classifier. The classifier takes these three measurements for a particular variant as input and provides a prediction as to whether the mutation is likely to be cancer-associated or not.